| Trajectory-based modeling of human actions with motion reference points YG Jiang, Q Dai, X Xue, W Liu, CW Ngo European Conference on Computer Vision, 425-438, 2012 | 264 | 2012 |
| Human action recognition in unconstrained videos by explicit motion modeling YG Jiang, Q Dai, W Liu, X Xue, CW Ngo IEEE Transactions on Image Processing 24 (11), 3781-3795, 2015 | 66 | 2015 |
| Fudan-Huawei at MediaEval 2015: Detecting Violent Scenes and Affective Impact in Movies with Deep Learning. Q Dai, RW Zhao, Z Wu, X Wang, Z Gu, W Wu, YG Jiang MediaEval, 2015 | 56 | 2015 |
| Learning spatial awareness to improve crowd counting ZQ Cheng, JX Li, Q Dai, X Wu, AG Hauptmann Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 55 | 2019 |
| Fast semantic diffusion for large-scale context-based image and video annotation YG Jiang, Q Dai, J Wang, CW Ngo, X Xue, SF Chang IEEE Transactions on Image Processing 21 (6), 3080-3091, 2012 | 55 | 2012 |
| Recurrent tubelet proposal and recognition networks for action detection D Li, Z Qiu, Q Dai, T Yao, T Mei Proceedings of the European conference on computer vision (ECCV), 303-318, 2018 | 54 | 2018 |
| Super fast event recognition in internet videos YG Jiang, Q Dai, T Mei, Y Rui, SF Chang IEEE Transactions on Multimedia 17 (8), 1174-1186, 2015 | 49 | 2015 |
| Binary optimized hashing Q Dai, J Li, J Wang, YG Jiang Proceedings of the 24th ACM international conference on Multimedia, 1247-1256, 2016 | 35 | 2016 |
| Beauty is here: Evaluating aesthetics in videos using multimodal features and free training data Y Wang, Q Dai, R Feng, YG Jiang Proceedings of the 21st ACM international conference on Multimedia, 369-372, 2013 | 32 | 2013 |
| Deep incremental hashing network for efficient image retrieval D Wu, Q Dai, J Liu, B Li, W Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 30 | 2019 |
| Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting ZQ Cheng, JX Li, Q Dai, X Wu, JY He, A Hauptmann Proceedings of the 27th ACM International Conference on Multimedia, 1897-1906, 2019 | 22 | 2019 |
| Fudan-NJUST at MediaEval 2014: Violent Scenes Detection Using Deep Neural Networks. Q Dai, Z Wu, YG Jiang, X Xue, J Tang MediaEval, 2014 | 22 | 2014 |
| Fudan at MediaEval 2013: Violent Scenes Detection Using Motion Features and Part-Level Attributes. Q Dai, J Tu, Z Shi, YG Jiang, X Xue MediaEval, 2013 | 17 | 2013 |
| Decoupling Localization and Classification in Single Shot Temporal Action Detection Y Huang, Q Dai, Y Lu 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019 | 14 | 2019 |
| Challenge Huawei challenge: Fusing multimodal features with deep neural networks for Mobile Video Annotation J Tu, Z Wu, Q Dai, YG Jiang, X Xue 2014 IEEE international conference on multimedia and expo workshops (ICMEW), 1-6, 2014 | 11 | 2014 |
| A Bayesian Hashing approach and its application to face recognition Q Dai, J Li, J Wang, Y Chen, YG Jiang Neurocomputing 213, 5-13, 2016 | 10 | 2016 |
| Weakly-supervised action localization by generative attention modeling B Shi, Q Dai, Y Mu, J Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 9 | 2020 |
| Deep domain adaptation hashing with adversarial learning F Long, T Yao, Q Dai, X Tian, J Luo, T Mei The 41st International ACM SIGIR Conference on Research & Development in …, 2018 | 9 | 2018 |
| The Shanghai-Hongkong Team at MediaEval2012: Violent Scene Detection Using Trajectory-based Features. YG Jiang, Q Dai, CC Tan, X Xue, CW Ngo MediaEval, 2012 | 7 | 2012 |
| Optimal Bayesian Hashing for Efficient Face Recognition. Q Dai, J Li, J Wang, Y Chen, YG Jiang IJCAI, 3430-3437, 2015 | 3 | 2015 |
Yu-Gang JiangProfessor, Computer Science, Fudan University, ShanghaiVerified email at fudan.edu.cn
